Identification of emotions in text articles through data pre-processing and data mining techniques

GeethaRamani, R.; Kumar, Mukesh; Balasubramanian, Lakshmi

doi:10.1109/icaccct.2016.7831713

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…may represent anger and frustration. Nevertheless, certain words may possess overlapping potential emotions; for instance, the word "Aww", can convey both pleasant sentiments and expressions of pity and sympathy [33,34]. Various applications leveraging data and text mining for the automatic recognition of sentiments or emotions can be observed, particularly in eliciting opinions related to marketing or promotional content from sources like blog posts, social media, articles, surveys, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Determinants of Emotion Recognition System Adoption: Empirical Evidence from Malaysia

Mohd Yamin,

Ab. Aziz,

Gek Siang

et al. 2023

Applied Sciences

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Emotion recognition systems (ERS) are an emerging technology with immense potential, exemplifying the innovative utilization of artificial intelligence (AI) within the context of the fourth industrial revolution (IR 4.0). Given that personalization is a key feature of the fifth industrial revolution (IR 5.0), ERS has the potential to serve as an enabler for IR 5.0. Furthermore, the COVID-19 pandemic has increased the relevance of this technology as work processes were adapted for social distancing and the use of face masks. Even in the post-pandemic era, many individuals continue to wear face masks. Therefore, ERS offers a technological solution to address communication challenges in a masked world. The existing body of knowledge on ERS primarily focuses on exploring modalities or modes for emotion recognition, system development, and the creation of applications utilizing emotion recognition functions. However, to enhance the development of impactful ERS, it is essential for researchers and innovators to understand the factors that influence its usage and adoption among the intended users. Therefore, this study presents a framework that combines technology adoption theories to identify the determinants of ERS adoption among Malaysian youth. Data for this study were collected through a survey involving 386 respondents. The findings revealed attitudes, subjective norms, perceived behavioral control, and awareness as significant determinants of ERS adoption. Additionally, the study found that technology aptitude plays a moderating role. These insights can inform the formulation of effective policies and programs to encourage and facilitate the development of innovative ERS solutions.

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Section: Literature Reviewmentioning

confidence: 99%

Determinants of Emotion Recognition System Adoption: Empirical Evidence from Malaysia

Mohd Yamin,

Ab. Aziz,

Gek Siang

et al. 2023

Applied Sciences

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“…The value of the method accuracy was obtained by dividing the number of true documents to true value with the number of all classified documents [11] [30].…”

Section: • Confusion Matrixmentioning

confidence: 99%

“…From several stages, the most regularly applied pre-processing is stopword removal [10]. This stage is done to eliminate words that do not affect the classification process [11] [12].…”

Section: Introductionmentioning

confidence: 99%

Journal Classification Using Cosine Similarity Method on Title and Abstract with Frequency-Based Stopword Removal

Nurfadila

Wibawa²,

Zaeni

et al. 2019

Int. J. Art. Intell. Research

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Classification of economic journal articles has been done using the VSM (Vector Space Model) approach and the Cosine Similarity method. The results of previous studies are considered to be less optimal because Stopword Removal was carried out by using a dictionary of basic words (tuning). Therefore, the omitted words limited to only basic words. This study shows the improved performance accuracy of the Cosine Similarity method using frequency-based Stopword Removal. The reason is because the term with a certain frequency is assumed to be an insignificant word and will give less relevant results. Performance testing of the Cosine Similarity method that had been added to frequency-based Stopword Removal was done by using K-fold Cross Validation. The method performance produced accuracy value for 64.28%, precision for 64.76 %, and recall for 65.26%. The execution time after pre-processing was 0, 05033 second.

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A Review of Tools and Techniques for Preprocessing of Textual Data

Kathuria

Gupta

Singla

2020

Advances in Intelligent Systems and Computing

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Identification of emotions in text articles through data pre-processing and data mining techniques

Cited by 4 publications

References 17 publications

Determinants of Emotion Recognition System Adoption: Empirical Evidence from Malaysia

Determinants of Emotion Recognition System Adoption: Empirical Evidence from Malaysia

Journal Classification Using Cosine Similarity Method on Title and Abstract with Frequency-Based Stopword Removal

A Review of Tools and Techniques for Preprocessing of Textual Data

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